Overview

Dataset statistics

Number of variables20
Number of observations1850
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory393.0 KiB
Average record size in memory217.5 B

Variable types

Numeric16
Categorical4

Alerts

PewQChng is highly imbalanced (50.6%)Imbalance
STATE is uniformly distributedUniform
GunsAmmo has unique valuesUnique
BS1 has 50 (2.7%) zerosZeros
BS2 has 400 (21.6%) zerosZeros
BS3 has 1000 (54.1%) zerosZeros

Reproduction

Analysis started2024-05-20 14:16:44.638655
Analysis finished2024-05-20 14:17:01.056537
Duration16.42 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

FIP
Real number (ℝ)

Distinct50
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.32
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-05-20T16:17:01.108208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q117
median29.5
Q342
95-th percentile54
Maximum56
Range55
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.627847
Coefficient of variation (CV)0.5330098
Kurtosis-1.0727969
Mean29.32
Median Absolute Deviation (MAD)12.5
Skewness-0.058522725
Sum54242
Variance244.22962
MonotonicityIncreasing
2024-05-20T16:17:01.189673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 37
 
2.0%
42 37
 
2.0%
32 37
 
2.0%
33 37
 
2.0%
34 37
 
2.0%
35 37
 
2.0%
36 37
 
2.0%
37 37
 
2.0%
38 37
 
2.0%
39 37
 
2.0%
Other values (40) 1480
80.0%
ValueCountFrequency (%)
1 37
2.0%
2 37
2.0%
4 37
2.0%
5 37
2.0%
6 37
2.0%
8 37
2.0%
9 37
2.0%
10 37
2.0%
12 37
2.0%
13 37
2.0%
ValueCountFrequency (%)
56 37
2.0%
55 37
2.0%
54 37
2.0%
53 37
2.0%
51 37
2.0%
50 37
2.0%
49 37
2.0%
48 37
2.0%
47 37
2.0%
46 37
2.0%

Year
Real number (ℝ)

Distinct37
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1998
Minimum1980
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-05-20T16:17:01.276247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1981
Q11989
median1998
Q32007
95-th percentile2015
Maximum2016
Range36
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.679965
Coefficient of variation (CV)0.0053453279
Kurtosis-1.2017584
Mean1998
Median Absolute Deviation (MAD)9
Skewness0
Sum3696300
Variance114.06165
MonotonicityNot monotonic
2024-05-20T16:17:01.356320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1980 50
 
2.7%
1999 50
 
2.7%
2001 50
 
2.7%
2002 50
 
2.7%
2003 50
 
2.7%
2004 50
 
2.7%
2005 50
 
2.7%
2006 50
 
2.7%
2007 50
 
2.7%
2008 50
 
2.7%
Other values (27) 1350
73.0%
ValueCountFrequency (%)
1980 50
2.7%
1981 50
2.7%
1982 50
2.7%
1983 50
2.7%
1984 50
2.7%
1985 50
2.7%
1986 50
2.7%
1987 50
2.7%
1988 50
2.7%
1989 50
2.7%
ValueCountFrequency (%)
2016 50
2.7%
2015 50
2.7%
2014 50
2.7%
2013 50
2.7%
2012 50
2.7%
2011 50
2.7%
2010 50
2.7%
2009 50
2.7%
2008 50
2.7%
2007 50
2.7%

STATE
Categorical

UNIFORM 

Distinct50
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size118.4 KiB
Alabama
 
37
Idaho
 
37
Mississippi
 
37
Arizona
 
37
Arkansas
 
37
Other values (45)
1665 

Length

Max length14
Median length11.5
Mean length8.44
Min length4

Characters and Unicode

Total characters15614
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlabama
2nd rowAlabama
3rd rowAlabama
4th rowAlabama
5th rowAlabama

Common Values

ValueCountFrequency (%)
Alabama 37
 
2.0%
Idaho 37
 
2.0%
Mississippi 37
 
2.0%
Arizona 37
 
2.0%
Arkansas 37
 
2.0%
California 37
 
2.0%
Colorado 37
 
2.0%
Connecticut 37
 
2.0%
Delaware 37
 
2.0%
Florida 37
 
2.0%
Other values (40) 1480
80.0%

Length

2024-05-20T16:17:01.444659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new 148
 
6.7%
virginia 74
 
3.3%
south 74
 
3.3%
north 74
 
3.3%
carolina 74
 
3.3%
dakota 74
 
3.3%
alabama 37
 
1.7%
oklahoma 37
 
1.7%
nevada 37
 
1.7%
hampshire 37
 
1.7%
Other values (42) 1554
70.0%

Most occurring characters

ValueCountFrequency (%)
a 2109
13.5%
i 1443
 
9.2%
n 1295
 
8.3%
o 1221
 
7.8%
s 1110
 
7.1%
e 1036
 
6.6%
r 777
 
5.0%
t 629
 
4.0%
l 518
 
3.3%
h 481
 
3.1%
Other values (36) 4995
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13024
83.4%
Uppercase Letter 2220
 
14.2%
Space Separator 370
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2109
16.2%
i 1443
11.1%
n 1295
9.9%
o 1221
9.4%
s 1110
8.5%
e 1036
8.0%
r 777
 
6.0%
t 629
 
4.8%
l 518
 
4.0%
h 481
 
3.7%
Other values (14) 2405
18.5%
Uppercase Letter
ValueCountFrequency (%)
M 333
15.0%
N 296
13.3%
C 185
 
8.3%
I 185
 
8.3%
W 148
 
6.7%
A 148
 
6.7%
O 111
 
5.0%
D 111
 
5.0%
V 111
 
5.0%
K 74
 
3.3%
Other values (11) 518
23.3%
Space Separator
ValueCountFrequency (%)
370
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15244
97.6%
Common 370
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2109
13.8%
i 1443
 
9.5%
n 1295
 
8.5%
o 1221
 
8.0%
s 1110
 
7.3%
e 1036
 
6.8%
r 777
 
5.1%
t 629
 
4.1%
l 518
 
3.4%
h 481
 
3.2%
Other values (35) 4625
30.3%
Common
ValueCountFrequency (%)
370
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2109
13.5%
i 1443
 
9.2%
n 1295
 
8.3%
o 1221
 
7.8%
s 1110
 
7.1%
e 1036
 
6.6%
r 777
 
5.0%
t 629
 
4.0%
l 518
 
3.3%
h 481
 
3.1%
Other values (36) 4995
32.0%

HFR
Real number (ℝ)

Distinct562
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44100162
Minimum0.034
Maximum0.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-05-20T16:17:01.539031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.034
5-th percentile0.15445
Q10.36625
median0.462
Q30.547
95-th percentile0.63855
Maximum0.8
Range0.766
Interquartile range (IQR)0.18075

Descriptive statistics

Standard deviation0.14466213
Coefficient of variation (CV)0.32803083
Kurtosis-0.06757393
Mean0.44100162
Median Absolute Deviation (MAD)0.09
Skewness-0.55862018
Sum815.853
Variance0.020927131
MonotonicityNot monotonic
2024-05-20T16:17:01.638700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.541 13
 
0.7%
0.467 12
 
0.6%
0.397 11
 
0.6%
0.421 10
 
0.5%
0.391 10
 
0.5%
0.549 10
 
0.5%
0.464 10
 
0.5%
0.466 10
 
0.5%
0.423 10
 
0.5%
0.47 9
 
0.5%
Other values (552) 1745
94.3%
ValueCountFrequency (%)
0.034 1
0.1%
0.056 1
0.1%
0.062 1
0.1%
0.063 2
0.1%
0.064 1
0.1%
0.07 1
0.1%
0.072 2
0.1%
0.073 1
0.1%
0.074 1
0.1%
0.075 2
0.1%
ValueCountFrequency (%)
0.8 1
0.1%
0.789 1
0.1%
0.782 1
0.1%
0.778 1
0.1%
0.776 1
0.1%
0.775 1
0.1%
0.766 1
0.1%
0.761 1
0.1%
0.76 1
0.1%
0.759 1
0.1%

HFR_se
Real number (ℝ)

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.034279459
Minimum0.018
Maximum0.047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-05-20T16:17:01.719502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.018
5-th percentile0.021
Q10.031
median0.032
Q30.038
95-th percentile0.047
Maximum0.047
Range0.029
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.0072918945
Coefficient of variation (CV)0.21271906
Kurtosis-0.18582281
Mean0.034279459
Median Absolute Deviation (MAD)0.004
Skewness0.10887758
Sum63.417
Variance5.3171725 × 10-5
MonotonicityNot monotonic
2024-05-20T16:17:01.787473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.032 312
16.9%
0.031 289
15.6%
0.047 252
13.6%
0.036 245
13.2%
0.038 164
8.9%
0.035 121
 
6.5%
0.044 94
 
5.1%
0.028 81
 
4.4%
0.026 74
 
4.0%
0.021 64
 
3.5%
Other values (5) 154
8.3%
ValueCountFrequency (%)
0.018 36
 
1.9%
0.019 11
 
0.6%
0.02 37
 
2.0%
0.021 64
 
3.5%
0.026 74
 
4.0%
0.028 81
 
4.4%
0.029 59
 
3.2%
0.031 289
15.6%
0.032 312
16.9%
0.033 11
 
0.6%
ValueCountFrequency (%)
0.047 252
13.6%
0.044 94
 
5.1%
0.038 164
8.9%
0.036 245
13.2%
0.035 121
 
6.5%
0.033 11
 
0.6%
0.032 312
16.9%
0.031 289
15.6%
0.029 59
 
3.2%
0.028 81
 
4.4%

universl
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size104.9 KiB
0
1526 
1
324 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1850
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1526
82.5%
1 324
 
17.5%

Length

2024-05-20T16:17:01.863466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T16:17:01.928151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1526
82.5%
1 324
 
17.5%

Most occurring characters

ValueCountFrequency (%)
0 1526
82.5%
1 324
 
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1850
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1526
82.5%
1 324
 
17.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1526
82.5%
1 324
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1526
82.5%
1 324
 
17.5%

permit
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size104.9 KiB
0
1440 
1
410 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1850
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1440
77.8%
1 410
 
22.2%

Length

2024-05-20T16:17:01.997348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T16:17:02.060197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1440
77.8%
1 410
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 1440
77.8%
1 410
 
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1850
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1440
77.8%
1 410
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1440
77.8%
1 410
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1440
77.8%
1 410
 
22.2%

Fem_FS_S
Real number (ℝ)

Distinct1297
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.051957684
Minimum-9
Maximum0.92307692
Zeros6
Zeros (%)0.3%
Negative84
Negative (%)4.5%
Memory size14.6 KiB
2024-05-20T16:17:02.149417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile0.037582978
Q10.23529412
median0.35294118
Q30.48741987
95-th percentile0.65576509
Maximum0.92307692
Range9.9230769
Interquartile range (IQR)0.25212575

Descriptive statistics

Standard deviation1.9588539
Coefficient of variation (CV)-37.700948
Kurtosis16.844662
Mean-0.051957684
Median Absolute Deviation (MAD)0.12580026
Skewness-4.3205617
Sum-96.121715
Variance3.8371087
MonotonicityNot monotonic
2024-05-20T16:17:02.239784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9 84
 
4.5%
0.5 23
 
1.2%
0.333333333 21
 
1.1%
0.375 14
 
0.8%
0.25 14
 
0.8%
0.428571429 9
 
0.5%
0.2 9
 
0.5%
0.6 8
 
0.4%
0.461538462 8
 
0.4%
0.1 7
 
0.4%
Other values (1287) 1653
89.4%
ValueCountFrequency (%)
-9 84
4.5%
0 6
 
0.3%
0.020833333 1
 
0.1%
0.024390244 1
 
0.1%
0.036036036 1
 
0.1%
0.039473684 1
 
0.1%
0.04 1
 
0.1%
0.041666667 1
 
0.1%
0.045454545 3
 
0.2%
0.046511628 1
 
0.1%
ValueCountFrequency (%)
0.923076923 1
0.1%
0.852459016 1
0.1%
0.839285714 1
0.1%
0.824324324 1
0.1%
0.814285714 1
0.1%
0.8 1
0.1%
0.79245283 1
0.1%
0.789473684 1
0.1%
0.784313725 1
0.1%
0.781818182 1
0.1%

Male_FS_S
Real number (ℝ)

Distinct1703
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61672521
Minimum0.14492754
Maximum0.87301587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-05-20T16:17:02.328647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.14492754
5-th percentile0.34319228
Q10.56010417
median0.63141288
Q30.70540905
95-th percentile0.79350794
Maximum0.87301587
Range0.72808834
Interquartile range (IQR)0.14530489

Descriptive statistics

Standard deviation0.12904051
Coefficient of variation (CV)0.20923503
Kurtosis0.67565522
Mean0.61672521
Median Absolute Deviation (MAD)0.072473657
Skewness-0.86416288
Sum1140.9416
Variance0.016651454
MonotonicityNot monotonic
2024-05-20T16:17:02.424602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.666666667 11
 
0.6%
0.75 7
 
0.4%
0.571428571 5
 
0.3%
0.65 4
 
0.2%
0.714285714 4
 
0.2%
0.772727273 4
 
0.2%
0.7 4
 
0.2%
0.693548387 4
 
0.2%
0.3 3
 
0.2%
0.647887324 3
 
0.2%
Other values (1693) 1801
97.4%
ValueCountFrequency (%)
0.144927536 1
0.1%
0.18404908 1
0.1%
0.188679245 1
0.1%
0.190839695 1
0.1%
0.191489362 1
0.1%
0.195121951 1
0.1%
0.204545455 1
0.1%
0.214953271 1
0.1%
0.220125786 1
0.1%
0.220689655 1
0.1%
ValueCountFrequency (%)
0.873015873 1
0.1%
0.872727273 1
0.1%
0.870535714 1
0.1%
0.867647059 1
0.1%
0.866141732 1
0.1%
0.862068966 1
0.1%
0.860240964 1
0.1%
0.849315068 1
0.1%
0.847736626 1
0.1%
0.846689895 1
0.1%

BRFSS
Real number (ℝ)

Distinct149
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.2483152
Minimum-9
Maximum0.63524969
Zeros0
Zeros (%)0.0%
Negative1702
Negative (%)92.0%
Memory size14.6 KiB
2024-05-20T16:17:02.518503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile-9
Q1-9
median-9
Q3-9
95-th percentile0.38293748
Maximum0.63524969
Range9.6352497
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.550064
Coefficient of variation (CV)-0.3091618
Kurtosis7.6214973
Mean-8.2483152
Median Absolute Deviation (MAD)0
Skewness3.1000371
Sum-15259.383
Variance6.5028264
MonotonicityNot monotonic
2024-05-20T16:17:02.726814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9 1702
92.0%
0.185761964 1
 
0.1%
0.410856653 1
 
0.1%
0.40784309 1
 
0.1%
0.497766222 1
 
0.1%
0.539406756 1
 
0.1%
0.553665012 1
 
0.1%
0.307065789 1
 
0.1%
0.303768532 1
 
0.1%
0.300320643 1
 
0.1%
Other values (139) 139
 
7.5%
ValueCountFrequency (%)
-9 1702
92.0%
0.10404954 1
 
0.1%
0.104968563 1
 
0.1%
0.107239969 1
 
0.1%
0.109076253 1
 
0.1%
0.114084688 1
 
0.1%
0.11669447 1
 
0.1%
0.119591883 1
 
0.1%
0.121652103 1
 
0.1%
0.124708772 1
 
0.1%
ValueCountFrequency (%)
0.635249688 1
0.1%
0.632222477 1
0.1%
0.631548212 1
0.1%
0.628999887 1
0.1%
0.616017899 1
0.1%
0.615808639 1
0.1%
0.600802187 1
0.1%
0.592589806 1
0.1%
0.592371532 1
0.1%
0.586465114 1
0.1%

GALLUP
Real number (ℝ)

Distinct637
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.7342099
Minimum-9
Maximum0.79562283
Zeros0
Zeros (%)0.0%
Negative1214
Negative (%)65.6%
Memory size14.6 KiB
2024-05-20T16:17:02.807905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile-9
Q1-9
median-9
Q30.43378104
95-th percentile0.64755141
Maximum0.79562283
Range9.7956228
Interquartile range (IQR)9.433781

Descriptive statistics

Standard deviation4.5140673
Coefficient of variation (CV)-0.78721696
Kurtosis-1.5651155
Mean-5.7342099
Median Absolute Deviation (MAD)0
Skewness0.65950602
Sum-10608.288
Variance20.376804
MonotonicityNot monotonic
2024-05-20T16:17:02.891681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9 1214
65.6%
0.545674194 1
 
0.1%
0.522304067 1
 
0.1%
0.502141444 1
 
0.1%
0.680883558 1
 
0.1%
0.628150848 1
 
0.1%
0.602486015 1
 
0.1%
0.61540476 1
 
0.1%
0.594737537 1
 
0.1%
0.598846513 1
 
0.1%
Other values (627) 627
33.9%
ValueCountFrequency (%)
-9 1214
65.6%
0.089905422 1
 
0.1%
0.095308083 1
 
0.1%
0.100570412 1
 
0.1%
0.10631454 1
 
0.1%
0.118515922 1
 
0.1%
0.127507526 1
 
0.1%
0.129337078 1
 
0.1%
0.130554653 1
 
0.1%
0.134868474 1
 
0.1%
ValueCountFrequency (%)
0.795622832 1
0.1%
0.795004217 1
0.1%
0.788352465 1
0.1%
0.782158534 1
0.1%
0.779946528 1
0.1%
0.768345462 1
0.1%
0.764209835 1
0.1%
0.757346703 1
0.1%
0.755312496 1
0.1%
0.755050416 1
0.1%

GSS
Real number (ℝ)

Distinct836
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.7541266
Minimum-9
Maximum0.72988636
Zeros0
Zeros (%)0.0%
Negative1015
Negative (%)54.9%
Memory size14.6 KiB
2024-05-20T16:17:02.973380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile-9
Q1-9
median-9
Q30.402183
95-th percentile0.56361329
Maximum0.72988636
Range9.7298864
Interquartile range (IQR)9.402183

Descriptive statistics

Standard deviation4.683266
Coefficient of variation (CV)-0.98509493
Kurtosis-1.9620484
Mean-4.7541266
Median Absolute Deviation (MAD)0
Skewness0.19671426
Sum-8795.1342
Variance21.932981
MonotonicityNot monotonic
2024-05-20T16:17:03.053716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9 1015
54.9%
0.323793845 1
 
0.1%
0.502682215 1
 
0.1%
0.52190833 1
 
0.1%
0.472910266 1
 
0.1%
0.51741746 1
 
0.1%
0.344395968 1
 
0.1%
0.469028314 1
 
0.1%
0.5062317 1
 
0.1%
0.353670671 1
 
0.1%
Other values (826) 826
44.6%
ValueCountFrequency (%)
-9 1015
54.9%
0.076028006 1
 
0.1%
0.09257 1
 
0.1%
0.097415767 1
 
0.1%
0.105265354 1
 
0.1%
0.117171402 1
 
0.1%
0.118503314 1
 
0.1%
0.121486008 1
 
0.1%
0.123381395 1
 
0.1%
0.126087303 1
 
0.1%
ValueCountFrequency (%)
0.729886358 1
0.1%
0.7130417 1
0.1%
0.70020355 1
0.1%
0.692423414 1
0.1%
0.687492756 1
0.1%
0.671626158 1
0.1%
0.671488121 1
0.1%
0.66737844 1
0.1%
0.663377007 1
0.1%
0.662105594 1
0.1%

PEW
Real number (ℝ)

Distinct584
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.0245448
Minimum-9
Maximum0.7348463
Zeros0
Zeros (%)0.0%
Negative1267
Negative (%)68.5%
Memory size14.6 KiB
2024-05-20T16:17:03.140761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile-9
Q1-9
median-9
Q30.34628227
95-th percentile0.57061333
Maximum0.7348463
Range9.7348463
Interquartile range (IQR)9.3462823

Descriptive statistics

Standard deviation4.3882251
Coefficient of variation (CV)-0.72839114
Kurtosis-1.3642057
Mean-6.0245448
Median Absolute Deviation (MAD)0
Skewness0.79744806
Sum-11145.408
Variance19.256519
MonotonicityNot monotonic
2024-05-20T16:17:03.229850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9 1267
68.5%
0.395153779 1
 
0.1%
0.597014914 1
 
0.1%
0.558776441 1
 
0.1%
0.575169469 1
 
0.1%
0.578879481 1
 
0.1%
0.568746829 1
 
0.1%
0.662695277 1
 
0.1%
0.534076593 1
 
0.1%
0.640037169 1
 
0.1%
Other values (574) 574
31.0%
ValueCountFrequency (%)
-9 1267
68.5%
0.066897042 1
 
0.1%
0.094564791 1
 
0.1%
0.098185521 1
 
0.1%
0.101437011 1
 
0.1%
0.113284785 1
 
0.1%
0.114904506 1
 
0.1%
0.122503138 1
 
0.1%
0.126661646 1
 
0.1%
0.127231275 1
 
0.1%
ValueCountFrequency (%)
0.734846304 1
0.1%
0.732214817 1
0.1%
0.725993231 1
0.1%
0.690808908 1
0.1%
0.689553417 1
0.1%
0.6872821 1
0.1%
0.686877765 1
0.1%
0.685480695 1
0.1%
0.685265753 1
0.1%
0.682871621 1
0.1%

HuntLic
Real number (ℝ)

Distinct1849
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37115946
Minimum-9
Maximum1.1380836
Zeros2
Zeros (%)0.1%
Negative1
Negative (%)0.1%
Memory size14.6 KiB
2024-05-20T16:17:03.316725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile0.15294918
Q10.24692434
median0.34125702
Q30.46222734
95-th percentile0.73093397
Maximum1.1380836
Range10.138084
Interquartile range (IQR)0.21530301

Descriptive statistics

Standard deviation0.28079291
Coefficient of variation (CV)0.75652903
Kurtosis670.79437
Mean0.37115946
Median Absolute Deviation (MAD)0.10544078
Skewness-19.851995
Sum686.645
Variance0.078844656
MonotonicityNot monotonic
2024-05-20T16:17:03.402142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
0.1%
0.291102487 1
 
0.1%
0.77769204 1
 
0.1%
0.744673762 1
 
0.1%
0.759539021 1
 
0.1%
0.752608904 1
 
0.1%
0.736944687 1
 
0.1%
0.745531158 1
 
0.1%
0.813181776 1
 
0.1%
0.795023278 1
 
0.1%
Other values (1839) 1839
99.4%
ValueCountFrequency (%)
-9 1
0.1%
0 2
0.1%
0.07304418 1
0.1%
0.074162268 1
0.1%
0.074327065 1
0.1%
0.077719016 1
0.1%
0.077782371 1
0.1%
0.081487522 1
0.1%
0.08198956 1
0.1%
0.082202054 1
0.1%
ValueCountFrequency (%)
1.138083636 1
0.1%
1.116874313 1
0.1%
1.076257185 1
0.1%
1.035560082 1
0.1%
0.998176429 1
0.1%
0.993818934 1
0.1%
0.992357659 1
0.1%
0.974616389 1
0.1%
0.965125382 1
0.1%
0.961170727 1
0.1%

GunsAmmo
Real number (ℝ)

UNIQUE 

Distinct1850
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.4864793 × 10-12
Minimum-3.4664823
Maximum3.9346176
Zeros0
Zeros (%)0.0%
Negative959
Negative (%)51.8%
Memory size14.6 KiB
2024-05-20T16:17:03.490570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.4664823
5-th percentile-1.4629333
Q1-0.64985488
median-0.033035082
Q30.42252771
95-th percentile2.1982075
Maximum3.9346176
Range7.4010999
Interquartile range (IQR)1.0723826

Descriptive statistics

Standard deviation0.99021716
Coefficient of variation (CV)-1.5265865 × 1011
Kurtosis2.0942085
Mean-6.4864793 × 10-12
Median Absolute Deviation (MAD)0.54427114
Skewness1.0304661
Sum-1.1999993 × 10-8
Variance0.98053002
MonotonicityNot monotonic
2024-05-20T16:17:03.576506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.509164477 1
 
0.1%
1.190292909 1
 
0.1%
3.754689916 1
 
0.1%
1.016878704 1
 
0.1%
0.982405746 1
 
0.1%
0.813016537 1
 
0.1%
0.599170359 1
 
0.1%
0.508019841 1
 
0.1%
0.459925001 1
 
0.1%
0.523298664 1
 
0.1%
Other values (1840) 1840
99.5%
ValueCountFrequency (%)
-3.466482311 1
0.1%
-2.244039378 1
0.1%
-2.072159372 1
0.1%
-1.940371448 1
0.1%
-1.937022554 1
0.1%
-1.933046215 1
0.1%
-1.905831042 1
0.1%
-1.895529289 1
0.1%
-1.893376663 1
0.1%
-1.862518835 1
0.1%
ValueCountFrequency (%)
3.934617582 1
0.1%
3.889746139 1
0.1%
3.823435325 1
0.1%
3.754689916 1
0.1%
3.690204196 1
0.1%
3.669083615 1
0.1%
3.639213177 1
0.1%
3.592401299 1
0.1%
3.489793152 1
0.1%
3.484267309 1
0.1%

BackChk
Real number (ℝ)

Distinct901
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.6216216
Minimum-9
Maximum2.8224725
Zeros0
Zeros (%)0.0%
Negative1440
Negative (%)77.8%
Memory size14.6 KiB
2024-05-20T16:17:03.662114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile-9
Q1-9
median-9
Q3-0.13412538
95-th percentile1.3866047
Maximum2.8224725
Range11.822473
Interquartile range (IQR)8.8658746

Descriptive statistics

Standard deviation4.5522705
Coefficient of variation (CV)-0.9849942
Kurtosis-1.8897099
Mean-4.6216216
Median Absolute Deviation (MAD)0
Skewness0.12479726
Sum-8550
Variance20.723167
MonotonicityNot monotonic
2024-05-20T16:17:03.747654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9 950
51.4%
-1.43818533 1
 
0.1%
-1.214437724 1
 
0.1%
0.497243821 1
 
0.1%
0.691823462 1
 
0.1%
0.658319889 1
 
0.1%
0.781906326 1
 
0.1%
0.873669106 1
 
0.1%
0.923318935 1
 
0.1%
0.969271359 1
 
0.1%
Other values (891) 891
48.2%
ValueCountFrequency (%)
-9 950
51.4%
-1.695030301 1
 
0.1%
-1.692557192 1
 
0.1%
-1.657150216 1
 
0.1%
-1.645371689 1
 
0.1%
-1.627720109 1
 
0.1%
-1.601762893 1
 
0.1%
-1.595680535 1
 
0.1%
-1.591555078 1
 
0.1%
-1.572404232 1
 
0.1%
ValueCountFrequency (%)
2.822472528 1
0.1%
2.822310135 1
0.1%
2.690803961 1
0.1%
2.679636304 1
0.1%
2.653296906 1
0.1%
2.537884945 1
0.1%
2.435559051 1
0.1%
2.430162701 1
0.1%
2.382433924 1
0.1%
2.370964485 1
0.1%

PewQChng
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size104.9 KiB
0
1650 
1
200 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1850
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1650
89.2%
1 200
 
10.8%

Length

2024-05-20T16:17:03.822265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T16:17:03.883980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1650
89.2%
1 200
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 1650
89.2%
1 200
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1850
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1650
89.2%
1 200
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1650
89.2%
1 200
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1650
89.2%
1 200
 
10.8%

BS1
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7567568
Minimum0
Maximum12
Zeros50
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-05-20T16:17:03.947805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18.73
median12
Q312
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)3.27

Descriptive statistics

Standard deviation3.581862
Coefficient of variation (CV)0.36711605
Kurtosis0.79936195
Mean9.7567568
Median Absolute Deviation (MAD)0
Skewness-1.4749712
Sum18050
Variance12.829735
MonotonicityNot monotonic
2024-05-20T16:17:04.022039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
12 1000
54.1%
1 50
 
2.7%
11.91 50
 
2.7%
11.73 50
 
2.7%
11.45 50
 
2.7%
11.09 50
 
2.7%
10.64 50
 
2.7%
10.09 50
 
2.7%
9.45 50
 
2.7%
0 50
 
2.7%
Other values (8) 400
 
21.6%
ValueCountFrequency (%)
0 50
2.7%
1 50
2.7%
2 50
2.7%
3 50
2.7%
4 50
2.7%
5 50
2.7%
6 50
2.7%
7 50
2.7%
7.91 50
2.7%
8.73 50
2.7%
ValueCountFrequency (%)
12 1000
54.1%
11.91 50
 
2.7%
11.73 50
 
2.7%
11.45 50
 
2.7%
11.09 50
 
2.7%
10.64 50
 
2.7%
10.09 50
 
2.7%
9.45 50
 
2.7%
8.73 50
 
2.7%
7.91 50
 
2.7%

BS2
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6
Minimum0
Maximum12
Zeros400
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-05-20T16:17:04.089226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.27
median6
Q311.73
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)11.46

Descriptive statistics

Standard deviation5.0611875
Coefficient of variation (CV)0.84353126
Kurtosis-1.7500815
Mean6
Median Absolute Deviation (MAD)5.73
Skewness-6.6468179 × 10-18
Sum11100
Variance25.615619
MonotonicityNot monotonic
2024-05-20T16:17:04.161792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 400
21.6%
12 400
21.6%
0.27 50
 
2.7%
7.91 50
 
2.7%
11.91 50
 
2.7%
11.73 50
 
2.7%
11.45 50
 
2.7%
11.09 50
 
2.7%
10.64 50
 
2.7%
10.09 50
 
2.7%
Other values (13) 650
35.1%
ValueCountFrequency (%)
0 400
21.6%
0.09 50
 
2.7%
0.27 50
 
2.7%
0.55 50
 
2.7%
0.91 50
 
2.7%
1.36 50
 
2.7%
1.91 50
 
2.7%
2.55 50
 
2.7%
3.27 50
 
2.7%
4.09 50
 
2.7%
ValueCountFrequency (%)
12 400
21.6%
11.91 50
 
2.7%
11.73 50
 
2.7%
11.45 50
 
2.7%
11.09 50
 
2.7%
10.64 50
 
2.7%
10.09 50
 
2.7%
9.45 50
 
2.7%
8.73 50
 
2.7%
7.91 50
 
2.7%

BS3
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2432432
Minimum0
Maximum12
Zeros1000
Zeros (%)54.1%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-05-20T16:17:04.228577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.27
95-th percentile11
Maximum12
Range12
Interquartile range (IQR)3.27

Descriptive statistics

Standard deviation3.581862
Coefficient of variation (CV)1.5967337
Kurtosis0.79936195
Mean2.2432432
Median Absolute Deviation (MAD)0
Skewness1.4749712
Sum4150
Variance12.829735
MonotonicityNot monotonic
2024-05-20T16:17:04.300666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 1000
54.1%
0.09 50
 
2.7%
11 50
 
2.7%
10 50
 
2.7%
9 50
 
2.7%
8 50
 
2.7%
7 50
 
2.7%
6 50
 
2.7%
5 50
 
2.7%
4.09 50
 
2.7%
Other values (8) 400
 
21.6%
ValueCountFrequency (%)
0 1000
54.1%
0.09 50
 
2.7%
0.27 50
 
2.7%
0.55 50
 
2.7%
0.91 50
 
2.7%
1.36 50
 
2.7%
1.91 50
 
2.7%
2.55 50
 
2.7%
3.27 50
 
2.7%
4.09 50
 
2.7%
ValueCountFrequency (%)
12 50
2.7%
11 50
2.7%
10 50
2.7%
9 50
2.7%
8 50
2.7%
7 50
2.7%
6 50
2.7%
5 50
2.7%
4.09 50
2.7%
3.27 50
2.7%

Interactions

2024-05-20T16:16:59.801895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-20T16:16:48.772239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:49.668670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:50.701913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:51.607240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:52.483969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:53.380560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:54.310861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:55.374873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:56.361162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:57.327312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:58.278508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:59.515501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:17:00.606623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:45.921609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:46.952457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:47.907677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:48.827688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:49.723184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:50.762674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:51.661663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:52.537562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:53.438134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:54.363404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:55.434330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:56.423466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:57.382639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:58.333573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:59.587516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:17:00.664510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:45.976261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:47.011789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:47.966632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:48.884961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:49.779005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:50.824673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:51.717304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:52.591712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:53.498479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:54.420093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:55.493549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:56.497550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:57.442346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:58.391248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:59.665284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:17:00.723630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:46.029735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:47.071971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:48.026230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:48.947886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:49.835039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:50.885509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:51.773572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:52.647631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:53.559324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:54.472472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:55.554815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:56.561265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:57.506227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:58.450529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T16:16:59.735197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-20T16:17:00.823581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-20T16:17:00.983430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

FIPYearSTATEHFRHFR_seuniverslpermitFem_FS_SMale_FS_SBRFSSGALLUPGSSPEWHuntLicGunsAmmoBackChkPewQChngBS1BS2BS3
011980Alabama0.6080.031000.8243240.833795-9.00.5539500.583632-9.00.291102-0.509164-9.000.000.000.0
111981Alabama0.5970.047000.6923080.831126-9.0-9.000000-9.000000-9.00.294962-0.618954-9.001.000.000.0
211982Alabama0.6610.036000.7717390.821429-9.0-9.0000000.655196-9.00.290545-0.526692-9.002.000.000.0
311983Alabama0.5860.038000.6881720.819277-9.00.611440-9.000000-9.00.284983-0.713227-9.003.000.000.0
411984Alabama0.6240.036000.7100000.775956-9.0-9.0000000.626933-9.00.281622-0.733305-9.004.000.000.0
511985Alabama0.6440.031000.7555560.835294-9.00.6119740.662106-9.00.278214-0.719122-9.005.000.000.0
611986Alabama0.5670.038000.6868690.777778-9.00.596843-9.000000-9.00.275302-0.732234-9.006.000.000.0
711987Alabama0.6090.036000.7113400.795455-9.0-9.0000000.586605-9.00.278852-0.842906-9.007.000.000.0
811988Alabama0.6060.031000.6380950.804071-9.00.7683450.505046-9.00.273267-0.835570-9.007.910.090.0
911989Alabama0.6270.031000.7142860.801471-9.00.7024640.583599-9.00.269243-0.646931-9.008.730.270.0
FIPYearSTATEHFRHFR_seuniverslpermitFem_FS_SMale_FS_SBRFSSGALLUPGSSPEWHuntLicGunsAmmoBackChkPewQChngBS1BS2BS3
1840562007Wyoming0.6210.032000.3888890.722892-9.0-9.00000-9.0000000.6463300.5535762.9406071.942821012.011.733.27
1841562008Wyoming0.6400.035000.5238100.737864-9.0-9.000000.582073-9.0000000.5603873.0315961.915723012.011.914.09
1842562009Wyoming0.6150.032000.6500000.758242-9.0-9.00000-9.0000000.5874790.5534333.0582511.735383012.012.005.00
1843562010Wyoming0.5720.028000.5200000.660377-9.0-9.000000.5052070.5756690.5607682.9968381.532140012.012.006.00
1844562011Wyoming0.5390.032000.4074070.695238-9.0-9.00000-9.0000000.5060780.5420452.8859451.627117012.012.007.00
1845562012Wyoming0.5970.029000.3750000.647482-9.00.66206-9.0000000.6222240.5456272.9739241.562481012.012.008.00
1846562013Wyoming0.6130.032000.5294120.714286-9.0-9.00000-9.0000000.6852660.5426973.0662521.638737112.012.009.00
1847562014Wyoming0.6080.044000.5833330.666667-9.0-9.00000-9.000000-9.0000000.5387613.0928541.603822112.012.0010.00
1848562015Wyoming0.5490.032000.3939390.661290-9.0-9.00000-9.0000000.6115310.5443562.9104141.348323112.012.0011.00
1849562016Wyoming0.6070.032000.3750000.669643-9.0-9.00000-9.0000000.7259930.5384392.8998401.265807112.012.0012.00